Finding experiments

To use incense we first have to instantiate an experiment loader that will enable us to query the database for specific runs.

targets_type iteration autoencoder_type batch_size artifacts
exp_id
50 Mnist False nomal_dim_tied_iteration 256 {'history_autoencoder_iteration': Artifact(nam...
51 Mnist False nomal_dim_tied_iteration 128 {'history_autoencoder_iteration': Artifact(nam...
52 Mnist False nomal_dim_tied_iteration 64 {'history_autoencoder_iteration': Artifact(nam...
53 Mnist False nomal_dim_tied_iteration 32 {'history_autoencoder_iteration': Artifact(nam...
54 10_Targets False nomal_dim_tied_iteration 256 {'history_autoencoder_iteration': Artifact(nam...
55 10_Targets False nomal_dim_tied_iteration 128 {'history_autoencoder_iteration': Artifact(nam...
56 10_Targets False nomal_dim_tied_iteration 64 {'history_autoencoder_iteration': Artifact(nam...
57 10_Targets False nomal_dim_tied_iteration 32 {'history_autoencoder_iteration': Artifact(nam...
targets_type iteration autoencoder_type batch_size artifacts sort
exp_id
54 10_Targets False nomal_dim_tied_iteration 256 {'history_autoencoder_iteration': Artifact(nam... 0
55 10_Targets False nomal_dim_tied_iteration 128 {'history_autoencoder_iteration': Artifact(nam... 1
56 10_Targets False nomal_dim_tied_iteration 64 {'history_autoencoder_iteration': Artifact(nam... 2
57 10_Targets False nomal_dim_tied_iteration 32 {'history_autoencoder_iteration': Artifact(nam... 3
50 Mnist False nomal_dim_tied_iteration 256 {'history_autoencoder_iteration': Artifact(nam... 4
51 Mnist False nomal_dim_tied_iteration 128 {'history_autoencoder_iteration': Artifact(nam... 5
52 Mnist False nomal_dim_tied_iteration 64 {'history_autoencoder_iteration': Artifact(nam... 6
53 Mnist False nomal_dim_tied_iteration 32 {'history_autoencoder_iteration': Artifact(nam... 7

Red best overall, and also best of subset. Bes means for accuracy max, rest min. Green best of subset.

predictions_df_0
Accuracy over iterations evaluations_feature_classifier
normal_dim_tied_iteration256 10_Targets normal_dim_tied_iteration128 10_Targets normal_dim_tied_iteration64 10_Targets normal_dim_tied_iteration32 10_Targets normal_dim_tied_iteration256 Mnist normal_dim_tied_iteration128 Mnist normal_dim_tied_iteration64 Mnist normal_dim_tied_iteration32 Mnist
0 0.8619 0.8368 0.7321 0.7162 0.9721 0.9693 0.9746 0.9722
1 0.695 0.675 0.483 0.5107 0.9675 0.961 0.9691 0.9673
2 0.6564 0.6269 0.4355 0.4745 0.9423 0.9471 0.9566 0.9583
3 0.6418 0.5991 0.4196 0.4656 0.8961 0.9286 0.9341 0.9401
4 0.6298 0.5899 0.4156 0.4629 0.8441 0.9046 0.9084 0.9122
5 0.6216 0.5883 0.4142 0.4616 0.7897 0.8787 0.8744 0.878
6 0.6149 0.5878 0.4138 0.461 0.7287 0.846 0.8349 0.8375
7 0.6111 0.5877 0.4136 0.461 0.6678 0.811 0.7949 0.7922
Loss over iterations autoencoder
normal_dim_tied_iteration256 10_Targets normal_dim_tied_iteration128 10_Targets normal_dim_tied_iteration64 10_Targets normal_dim_tied_iteration32 10_Targets normal_dim_tied_iteration256 Mnist normal_dim_tied_iteration128 Mnist normal_dim_tied_iteration64 Mnist normal_dim_tied_iteration32 Mnist
0 11.858 0.331733 1140.51 46157.5 0.0440683 0.0491838 0.042375 0.0401993
1 7.01651e+13 0.355304 2.55078e+16 2.10041e+19 0.0952618 0.061325 0.0759085 0.0607388
2 4.29388e+26 0.371302 5.72407e+29 inf 1.86603e+10 0.1422 1.57574e+11 850748
3 inf 0.383174 inf inf 1.6668e+22 2.99348e+11 2.4731e+24 3.50237e+14
4 inf 0.394647 inf inf inf 1.7959e+24 inf 1.44295e+23
5 inf 0.401297 inf nan inf inf inf inf
6 nan 0.403797 nan nan inf inf inf inf
7 nan 0.404489 nan nan inf inf nan inf
MAE over iterations autoencoder
normal_dim_tied_iteration256 10_Targets normal_dim_tied_iteration128 10_Targets normal_dim_tied_iteration64 10_Targets normal_dim_tied_iteration32 10_Targets normal_dim_tied_iteration256 Mnist normal_dim_tied_iteration128 Mnist normal_dim_tied_iteration64 Mnist normal_dim_tied_iteration32 Mnist
0 0.282601 0.258207 1.75176 6.79399 0.0800112 0.0868673 0.0773086 0.0745843
1 87692.5 0.259549 6.98239e+06 1.39292e+08 0.0984425 0.0972501 0.0907358 0.0878206
2 2.16932e+11 0.259907 3.30754e+13 2.9718e+15 3814.44 0.110695 4769.96 8.42949
3 5.36647e+17 0.260522 1.56682e+20 6.34032e+22 3.60532e+09 4297.57 1.88969e+10 169631
4 1.32756e+24 0.261125 7.42226e+26 1.3527e+30 3.40742e+15 1.05279e+10 7.48632e+16 3.44325e+09
5 3.28411e+30 0.261441 inf nan 3.22039e+21 2.57867e+16 2.96584e+23 6.989e+13
6 nan 0.2616 nan nan 3.04362e+27 6.31611e+22 1.17497e+30 1.4186e+18
7 nan 0.261658 nan nan inf 1.54705e+29 nan 2.87944e+22
predictions_df_10
Accuracy over iterations evaluations_feature_classifier
normal_dim_tied_iteration256 10_Targets normal_dim_tied_iteration128 10_Targets normal_dim_tied_iteration64 10_Targets normal_dim_tied_iteration32 10_Targets normal_dim_tied_iteration256 Mnist normal_dim_tied_iteration128 Mnist normal_dim_tied_iteration64 Mnist normal_dim_tied_iteration32 Mnist
0 0.8289 0.8031 0.713 0.6948 0.8885 0.8898 0.8568 0.8779
1 0.6774 0.6474 0.4786 0.4955 0.8229 0.8638 0.7727 0.8305
2 0.6393 0.5971 0.43 0.4641 0.757 0.8311 0.7102 0.7551
3 0.6264 0.5745 0.4139 0.456 0.7135 0.8018 0.6719 0.7102
4 0.6181 0.5674 0.409 0.4538 0.6681 0.7716 0.637 0.6722
5 0.6112 0.5649 0.4064 0.4523 0.6206 0.7392 0.6039 0.6338
6 0.6046 0.5647 0.4046 0.4523 0.5736 0.7045 0.5725 0.5961
7 0.6009 0.5645 0.4047 0.4523 0.5268 0.6701 0.5325 0.5566
Loss over iterations autoencoder
normal_dim_tied_iteration256 10_Targets normal_dim_tied_iteration128 10_Targets normal_dim_tied_iteration64 10_Targets normal_dim_tied_iteration32 10_Targets normal_dim_tied_iteration256 Mnist normal_dim_tied_iteration128 Mnist normal_dim_tied_iteration64 Mnist normal_dim_tied_iteration32 Mnist
0 22.5625 0.331953 507.613 18065.2 0.488009 0.892952 4.95772 0.744305
1 1.35371e+14 0.356419 1.13444e+16 8.22073e+18 1.92517e+11 3.47306e+12 4.83701e+13 1.08407e+08
2 8.28429e+26 0.372745 2.54573e+29 inf 1.71962e+23 2.08362e+25 7.5916e+26 4.463e+16
3 inf 0.384993 inf inf inf inf inf 1.83872e+25
4 inf 0.396741 inf inf inf inf inf inf
5 inf 0.403653 inf nan inf inf inf inf
6 nan 0.406193 nan nan inf nan nan inf
7 nan 0.406886 nan nan nan nan nan inf
MAE over iterations autoencoder
normal_dim_tied_iteration256 10_Targets normal_dim_tied_iteration128 10_Targets normal_dim_tied_iteration64 10_Targets normal_dim_tied_iteration32 10_Targets normal_dim_tied_iteration256 Mnist normal_dim_tied_iteration128 Mnist normal_dim_tied_iteration64 Mnist normal_dim_tied_iteration32 Mnist
0 0.307521 0.259895 0.967086 2.56955 0.187253 0.16305 0.382042 0.193636
1 146240 0.260879 3.308e+06 4.91593e+07 45098.7 89428 1.1445e+06 1224.8
2 3.61768e+11 0.261185 1.56699e+13 1.04881e+15 4.26259e+10 2.19099e+11 4.5342e+12 2.496e+07
3 8.9494e+17 0.261715 7.42302e+19 2.23763e+22 4.02862e+16 5.36654e+17 1.7963e+19 5.06653e+11
4 2.2139e+24 0.262176 3.51639e+26 4.77398e+29 3.80748e+22 1.31446e+24 7.11637e+25 1.02839e+16
5 5.47674e+30 0.262549 inf nan 3.59849e+28 3.2196e+30 2.81928e+32 2.08739e+20
6 nan 0.262756 nan nan inf nan nan 4.23692e+24
7 nan 0.262827 nan nan nan nan nan 8.59997e+28
predictions_df_20
Accuracy over iterations evaluations_feature_classifier
normal_dim_tied_iteration256 10_Targets normal_dim_tied_iteration128 10_Targets normal_dim_tied_iteration64 10_Targets normal_dim_tied_iteration32 10_Targets normal_dim_tied_iteration256 Mnist normal_dim_tied_iteration128 Mnist normal_dim_tied_iteration64 Mnist normal_dim_tied_iteration32 Mnist
0 0.7889 0.7647 0.6929 0.6638 0.7918 0.7956 0.7257 0.7789
1 0.6502 0.6298 0.4702 0.4861 0.7027 0.7651 0.5815 0.6866
2 0.6152 0.5846 0.4245 0.456 0.6282 0.7304 0.5207 0.601
3 0.6053 0.5622 0.4072 0.448 0.5891 0.6962 0.4917 0.5562
4 0.5967 0.5562 0.4014 0.4467 0.5427 0.6661 0.4642 0.5241
5 0.5924 0.554 0.3993 0.4457 0.5045 0.6389 0.4401 0.4901
6 0.5861 0.5531 0.3983 0.4452 0.4654 0.6095 0.4155 0.4586
7 0.5818 0.5528 0.398 0.445 0.4261 0.5771 0.3912 0.4294
Loss over iterations autoencoder
normal_dim_tied_iteration256 10_Targets normal_dim_tied_iteration128 10_Targets normal_dim_tied_iteration64 10_Targets normal_dim_tied_iteration32 10_Targets normal_dim_tied_iteration256 Mnist normal_dim_tied_iteration128 Mnist normal_dim_tied_iteration64 Mnist normal_dim_tied_iteration32 Mnist
0 28.1916 0.331852 367.036 9175.9 1.58508 25.2917 1658.99 2.99281
1 1.70015e+14 0.357046 8.2019e+15 4.17506e+18 8.24829e+11 1.40847e+14 2.57804e+16 5.68696e+08
2 1.04044e+27 0.373619 1.84054e+29 inf 7.36764e+23 8.44994e+26 4.0462e+29 2.34152e+17
3 inf 0.386271 inf inf inf inf inf 9.64689e+25
4 inf 0.398429 inf inf inf inf inf inf
5 inf 0.40529 inf nan inf inf inf inf
6 nan 0.407935 nan nan inf nan nan inf
7 nan 0.408804 nan nan nan nan nan inf
MAE over iterations autoencoder
normal_dim_tied_iteration256 10_Targets normal_dim_tied_iteration128 10_Targets normal_dim_tied_iteration64 10_Targets normal_dim_tied_iteration32 10_Targets normal_dim_tied_iteration256 Mnist normal_dim_tied_iteration128 Mnist normal_dim_tied_iteration64 Mnist normal_dim_tied_iteration32 Mnist
0 0.292307 0.261439 0.798007 1.95752 0.321486 0.299117 1.64464 0.377288
1 104532 0.262085 2.51678e+06 3.60469e+07 137016 392590 6.83856e+06 4520.07
2 2.58591e+11 0.262338 1.19219e+13 7.6906e+14 1.295e+11 9.61747e+11 2.70923e+13 9.20343e+07
3 6.39701e+17 0.262748 5.64757e+19 1.64079e+22 1.22392e+17 2.35567e+18 1.07331e+20 1.86815e+12
4 1.58249e+24 0.263168 2.67533e+26 3.50061e+29 1.15674e+23 5.7699e+24 4.2521e+26 3.79192e+16
5 3.91476e+30 0.263499 inf nan 1.09324e+29 1.41326e+31 inf 7.69671e+20
6 nan 0.263669 nan nan inf nan nan 1.56225e+25
7 nan 0.26375 nan nan nan nan nan 3.17101e+29
predictions_df_30
Accuracy over iterations evaluations_feature_classifier
normal_dim_tied_iteration256 10_Targets normal_dim_tied_iteration128 10_Targets normal_dim_tied_iteration64 10_Targets normal_dim_tied_iteration32 10_Targets normal_dim_tied_iteration256 Mnist normal_dim_tied_iteration128 Mnist normal_dim_tied_iteration64 Mnist normal_dim_tied_iteration32 Mnist
0 0.7295 0.7188 0.6602 0.6352 0.7123 0.7062 0.6092 0.678
1 0.6226 0.5947 0.4685 0.4713 0.6087 0.662 0.4505 0.5623
2 0.5965 0.5561 0.4117 0.4378 0.5338 0.6335 0.4017 0.4778
3 0.5862 0.5362 0.3957 0.4283 0.4909 0.6057 0.3767 0.4411
4 0.581 0.5289 0.3901 0.4273 0.4511 0.5801 0.3575 0.4154
5 0.5756 0.5272 0.388 0.4271 0.4146 0.5562 0.3396 0.3847
6 0.5706 0.5266 0.3872 0.4272 0.3816 0.5327 0.3197 0.3594
7 0.5662 0.5265 0.3864 0.4271 0.3578 0.5075 0.2944 0.3346
Loss over iterations autoencoder
normal_dim_tied_iteration256 10_Targets normal_dim_tied_iteration128 10_Targets normal_dim_tied_iteration64 10_Targets normal_dim_tied_iteration32 10_Targets normal_dim_tied_iteration256 Mnist normal_dim_tied_iteration128 Mnist normal_dim_tied_iteration64 Mnist normal_dim_tied_iteration32 Mnist
0 0.35147 0.332484 204.824 30276.8 2.49049 1107.42 6052.16 6.64916
1 1.13041e+11 0.357981 4.57489e+15 1.37789e+19 1.37425e+12 6.58275e+15 9.42148e+16 1.38866e+09
2 6.91776e+23 0.375042 1.02663e+29 inf 1.22752e+24 3.94925e+28 1.47869e+30 5.71789e+17
3 inf 0.388116 inf inf inf inf inf 2.35573e+26
4 inf 0.400985 inf inf inf inf inf inf
5 inf 0.408253 inf nan inf inf inf inf
6 nan 0.4109 nan nan inf nan nan inf
7 nan 0.411732 nan nan nan nan nan inf
MAE over iterations autoencoder
normal_dim_tied_iteration256 10_Targets normal_dim_tied_iteration128 10_Targets normal_dim_tied_iteration64 10_Targets normal_dim_tied_iteration32 10_Targets normal_dim_tied_iteration256 Mnist normal_dim_tied_iteration128 Mnist normal_dim_tied_iteration64 Mnist normal_dim_tied_iteration32 Mnist
0 0.252483 0.26392 0.520379 3.08801 0.416441 1.11994 4.48711 0.595055
1 2695.64 0.263854 1.21471e+06 6.00994e+07 197777 2.5163e+06 1.92178e+07 8955.93
2 6.66792e+09 0.263932 5.75406e+12 1.28222e+15 1.86926e+11 6.16374e+12 7.61351e+13 1.82292e+08
3 1.64951e+16 0.264135 2.72578e+19 2.73561e+22 1.76666e+17 1.50973e+19 3.01622e+20 3.70022e+12
4 4.08055e+22 0.264517 1.29124e+26 5.83641e+29 1.66969e+23 3.69787e+25 1.19493e+27 7.51059e+16
5 1.00945e+29 0.264861 inf nan 1.57804e+29 9.05743e+31 inf 1.52448e+21
6 nan 0.265062 nan nan inf nan nan 3.09433e+25
7 nan 0.265141 nan nan nan nan nan 6.28078e+29
predictions_df_40
Accuracy over iterations evaluations_feature_classifier
normal_dim_tied_iteration256 10_Targets normal_dim_tied_iteration128 10_Targets normal_dim_tied_iteration64 10_Targets normal_dim_tied_iteration32 10_Targets normal_dim_tied_iteration256 Mnist normal_dim_tied_iteration128 Mnist normal_dim_tied_iteration64 Mnist normal_dim_tied_iteration32 Mnist
0 0.6758 0.6712 0.6345 0.5949 0.6465 0.6307 0.5319 0.5941
1 0.594 0.5476 0.451 0.4426 0.5315 0.5912 0.3701 0.4646
2 0.5717 0.5193 0.4003 0.4106 0.4549 0.5582 0.3237 0.391
3 0.563 0.5002 0.3839 0.4063 0.4084 0.5308 0.3029 0.3593
4 0.5567 0.4942 0.3803 0.4052 0.376 0.506 0.2878 0.3331
5 0.5529 0.491 0.3792 0.4048 0.3467 0.4847 0.2741 0.3163
6 0.5483 0.4904 0.3783 0.4047 0.3172 0.4674 0.2642 0.2965
7 0.5437 0.4902 0.3779 0.4048 0.2961 0.4438 0.244 0.2796
Loss over iterations autoencoder
normal_dim_tied_iteration256 10_Targets normal_dim_tied_iteration128 10_Targets normal_dim_tied_iteration64 10_Targets normal_dim_tied_iteration32 10_Targets normal_dim_tied_iteration256 Mnist normal_dim_tied_iteration128 Mnist normal_dim_tied_iteration64 Mnist normal_dim_tied_iteration32 Mnist
0 0.334449 0.33355 166.198 272.237 3.66469 5455.45 6631.49 13.0949
1 7.08332e+06 0.360352 3.70999e+15 1.23569e+17 2.11366e+12 3.25487e+16 1.0285e+17 2.95056e+09
2 4.33474e+19 0.377944 8.32537e+28 5.62461e+31 1.88799e+24 1.95273e+29 1.61422e+30 1.21497e+18
3 inf 0.391355 inf inf inf inf inf 5.00559e+26
4 inf 0.404701 inf inf inf inf inf inf
5 inf 0.412552 inf nan inf inf inf inf
6 inf 0.415513 nan nan inf nan nan inf
7 nan 0.416366 nan nan nan nan nan inf
MAE over iterations autoencoder
normal_dim_tied_iteration256 10_Targets normal_dim_tied_iteration128 10_Targets normal_dim_tied_iteration64 10_Targets normal_dim_tied_iteration32 10_Targets normal_dim_tied_iteration256 Mnist normal_dim_tied_iteration128 Mnist normal_dim_tied_iteration64 Mnist normal_dim_tied_iteration32 Mnist
0 0.254736 0.26692 0.515336 0.491423 0.521977 2.91956 6.35372 0.872454
1 23.2818 0.266569 1.18494e+06 4.64837e+06 273318 7.18149e+06 2.7821e+07 15278.9
2 5.69535e+07 0.266385 5.61305e+12 9.91727e+13 2.58323e+11 1.75909e+13 1.10218e+14 3.10904e+08
3 1.40891e+14 0.266581 2.65898e+19 2.11585e+21 2.44144e+17 4.30864e+19 4.36648e+20 6.31083e+12
4 3.48537e+20 0.266916 1.25959e+26 4.51415e+28 2.30743e+23 1.05534e+26 1.72986e+27 1.28095e+17
5 8.6221e+26 0.267193 inf nan 2.18077e+29 2.58492e+32 inf 2.60004e+21
6 inf 0.267391 nan nan inf nan nan 5.27747e+25
7 nan 0.267474 nan nan nan nan nan 1.0712e+30
predictions_df_50
Accuracy over iterations evaluations_feature_classifier
normal_dim_tied_iteration256 10_Targets normal_dim_tied_iteration128 10_Targets normal_dim_tied_iteration64 10_Targets normal_dim_tied_iteration32 10_Targets normal_dim_tied_iteration256 Mnist normal_dim_tied_iteration128 Mnist normal_dim_tied_iteration64 Mnist normal_dim_tied_iteration32 Mnist
0 0.613 0.6099 0.5924 0.5624 0.5778 0.544 0.4557 0.5103
1 0.5501 0.5111 0.4397 0.4173 0.4568 0.5042 0.3028 0.3663
2 0.5353 0.4866 0.3854 0.3849 0.385 0.4765 0.2652 0.3058
3 0.5252 0.4703 0.3689 0.3817 0.3462 0.4542 0.2499 0.2813
4 0.5202 0.463 0.3622 0.3804 0.3121 0.435 0.2399 0.2675
5 0.5164 0.4611 0.3609 0.3802 0.2836 0.4168 0.2294 0.2525
6 0.5129 0.4608 0.3599 0.38 0.2574 0.4001 0.2161 0.2372
7 0.5099 0.461 0.3594 0.3797 0.2402 0.3795 0.2044 0.2254
Loss over iterations autoencoder
normal_dim_tied_iteration256 10_Targets normal_dim_tied_iteration128 10_Targets normal_dim_tied_iteration64 10_Targets normal_dim_tied_iteration32 10_Targets normal_dim_tied_iteration256 Mnist normal_dim_tied_iteration128 Mnist normal_dim_tied_iteration64 Mnist normal_dim_tied_iteration32 Mnist
0 1.58543 0.336038 95.394 1138.36 289.098 31656.9 17426.4 25.5181
1 7.50993e+12 8.00312e+09 2.12785e+15 5.17658e+17 2.56059e+14 1.89295e+17 2.71098e+17 6.11499e+09
2 4.59583e+25 7.23421e+24 4.77499e+28 inf 2.28721e+26 1.13565e+30 4.25484e+30 2.5181e+18
3 inf inf inf inf inf inf inf 1.03744e+27
4 inf inf inf inf inf inf inf inf
5 inf inf inf nan inf inf inf inf
6 nan nan nan nan inf nan nan inf
7 nan nan nan nan nan nan nan inf
MAE over iterations autoencoder
normal_dim_tied_iteration256 10_Targets normal_dim_tied_iteration128 10_Targets normal_dim_tied_iteration64 10_Targets normal_dim_tied_iteration32 10_Targets normal_dim_tied_iteration256 Mnist normal_dim_tied_iteration128 Mnist normal_dim_tied_iteration64 Mnist normal_dim_tied_iteration32 Mnist
0 0.266141 0.269467 0.38141 0.65657 0.762779 10.8588 11.151 1.2592
1 22251.5 709.866 553413 8.08543e+06 483673 2.75611e+07 4.85226e+07 24645.7
2 5.5045e+10 2.13386e+10 2.62156e+12 1.72502e+14 4.57132e+11 6.75091e+13 1.92232e+14 5.01398e+08
3 1.3617e+17 6.41552e+17 1.24187e+19 3.68033e+21 4.3204e+17 1.65354e+20 7.61559e+20 1.01775e+13
4 3.36858e+23 1.92885e+25 5.8829e+25 7.85197e+28 4.08325e+23 4.05013e+26 3.01705e+27 2.0658e+17
5 8.33318e+29 inf inf nan 3.85912e+29 9.92025e+32 inf 4.19309e+21
6 nan nan nan nan inf nan nan 8.511e+25
7 nan nan nan nan nan nan nan 1.72753e+30
predictions_df_60
Accuracy over iterations evaluations_feature_classifier
normal_dim_tied_iteration256 10_Targets normal_dim_tied_iteration128 10_Targets normal_dim_tied_iteration64 10_Targets normal_dim_tied_iteration32 10_Targets normal_dim_tied_iteration256 Mnist normal_dim_tied_iteration128 Mnist normal_dim_tied_iteration64 Mnist normal_dim_tied_iteration32 Mnist
0 0.5477 0.5464 0.5438 0.5003 0.5337 0.4792 0.3887 0.443
1 0.5041 0.4692 0.4078 0.3771 0.4083 0.438 0.2434 0.2985
2 0.4916 0.4487 0.3623 0.3516 0.3291 0.4141 0.2159 0.2439
3 0.4859 0.4362 0.3439 0.3473 0.289 0.3998 0.2035 0.2256
4 0.4836 0.4317 0.3401 0.346 0.261 0.3843 0.1918 0.2144
5 0.4809 0.43 0.3379 0.3467 0.2375 0.3724 0.1859 0.2036
6 0.4772 0.4295 0.3371 0.3464 0.2162 0.3543 0.1778 0.1907
7 0.4733 0.4292 0.3365 0.3462 0.2044 0.3355 0.1665 0.181
Loss over iterations autoencoder
normal_dim_tied_iteration256 10_Targets normal_dim_tied_iteration128 10_Targets normal_dim_tied_iteration64 10_Targets normal_dim_tied_iteration32 10_Targets normal_dim_tied_iteration256 Mnist normal_dim_tied_iteration128 Mnist normal_dim_tied_iteration64 Mnist normal_dim_tied_iteration32 Mnist
0 0.345818 0.338227 116.179 0.317512 192.055 56842.2 31114.9 38.1415
1 9.52198e+08 0.365955 2.59052e+15 0.383506 1.69408e+14 3.39952e+17 4.84942e+17 9.3244e+09
2 5.82714e+21 0.384757 5.81324e+28 1205.03 1.51321e+26 2.03951e+30 7.61109e+30 3.83973e+18
3 inf 0.398697 inf 5.4801e+17 inf inf inf 1.58195e+27
4 inf 0.412394 inf inf inf inf inf inf
5 inf 0.420297 inf inf inf inf inf inf
6 inf 0.423409 nan inf inf nan nan inf
7 nan 0.424448 nan nan nan nan nan inf
MAE over iterations autoencoder
normal_dim_tied_iteration256 10_Targets normal_dim_tied_iteration128 10_Targets normal_dim_tied_iteration64 10_Targets normal_dim_tied_iteration32 10_Targets normal_dim_tied_iteration256 Mnist normal_dim_tied_iteration128 Mnist normal_dim_tied_iteration64 Mnist normal_dim_tied_iteration32 Mnist
0 0.261175 0.27228 0.414712 0.281871 0.828239 19.355 16.2308 1.64474
1 291.453 0.271055 697474 0.291952 520851 4.93893e+07 7.01351e+07 34098.7
2 7.20409e+08 0.270727 3.30395e+12 0.586689 4.9227e+11 1.20975e+14 2.77854e+14 6.93647e+08
3 1.78215e+15 0.270666 1.56513e+19 6.14024e+06 4.65249e+17 2.96313e+20 1.10076e+21 1.40798e+13
4 4.40867e+21 0.270837 7.41422e+25 1.31002e+14 4.39712e+23 7.2578e+26 4.36087e+27 2.85788e+17
5 1.09062e+28 0.271108 inf 2.79492e+21 4.15576e+29 1.7777e+33 inf 5.80083e+21
6 inf 0.271319 nan 5.96294e+28 inf nan nan 1.17743e+26
7 nan 0.271411 nan nan nan nan nan 2.38992e+30
predictions_df_70
Accuracy over iterations evaluations_feature_classifier
normal_dim_tied_iteration256 10_Targets normal_dim_tied_iteration128 10_Targets normal_dim_tied_iteration64 10_Targets normal_dim_tied_iteration32 10_Targets normal_dim_tied_iteration256 Mnist normal_dim_tied_iteration128 Mnist normal_dim_tied_iteration64 Mnist normal_dim_tied_iteration32 Mnist
0 0.4783 0.4807 0.4929 0.4542 0.4733 0.4209 0.3451 0.3804
1 0.4577 0.4054 0.3859 0.3507 0.3517 0.3739 0.2119 0.2329
2 0.4469 0.3866 0.3428 0.3276 0.2807 0.3514 0.1815 0.1977
3 0.44 0.3738 0.3249 0.3234 0.248 0.3388 0.1716 0.1838
4 0.4368 0.3703 0.3207 0.3217 0.2246 0.3279 0.1661 0.1752
5 0.4345 0.3699 0.318 0.3209 0.2038 0.3144 0.1605 0.1665
6 0.4316 0.3694 0.3173 0.3212 0.1891 0.3002 0.1499 0.1595
7 0.4299 0.3695 0.3169 0.3211 0.18 0.287 0.1465 0.1523
Loss over iterations autoencoder
normal_dim_tied_iteration256 10_Targets normal_dim_tied_iteration128 10_Targets normal_dim_tied_iteration64 10_Targets normal_dim_tied_iteration32 10_Targets normal_dim_tied_iteration256 Mnist normal_dim_tied_iteration128 Mnist normal_dim_tied_iteration64 Mnist normal_dim_tied_iteration32 Mnist
0 0.350878 0.343233 93.8624 3436.76 265.853 127984 65187.2 63.0467
1 2.90657e+08 4.31318e+11 2.09122e+15 1.56376e+18 2.33798e+14 7.66012e+17 1.01754e+18 1.58977e+10
2 1.77872e+21 3.89879e+26 4.69278e+28 inf 2.08836e+26 4.59561e+30 1.59701e+31 6.5467e+18
3 inf inf inf inf inf inf inf 2.6972e+27
4 inf inf inf inf inf inf inf inf
5 inf inf inf nan inf inf inf inf
6 inf nan nan nan inf nan nan inf
7 nan nan nan nan nan nan nan inf
MAE over iterations autoencoder
normal_dim_tied_iteration256 10_Targets normal_dim_tied_iteration128 10_Targets normal_dim_tied_iteration64 10_Targets normal_dim_tied_iteration32 10_Targets normal_dim_tied_iteration256 Mnist normal_dim_tied_iteration128 Mnist normal_dim_tied_iteration64 Mnist normal_dim_tied_iteration32 Mnist
0 0.265043 0.276687 0.407133 0.804195 1.14616 34.5695 26.3127 2.21781
1 207.179 5210.41 655931 1.10637e+07 816719 8.80236e+07 1.12207e+08 48726.8
2 5.11911e+08 1.56652e+11 3.10726e+12 2.36043e+14 7.719e+11 2.15607e+14 4.44529e+14 9.91106e+08
3 1.26637e+15 4.70978e+18 1.47195e+19 5.03597e+21 7.2953e+17 5.281e+20 1.76108e+21 2.01177e+13
4 3.13273e+21 1.41601e+26 6.97283e+25 1.07442e+29 6.89487e+23 1.29351e+27 6.97682e+27 4.08342e+17
5 7.74974e+27 inf inf nan 6.51641e+29 inf inf 8.2884e+21
6 inf nan nan nan inf nan nan 1.68235e+26
7 nan nan nan nan nan nan nan 3.41479e+30
predictions_df_80
Accuracy over iterations evaluations_feature_classifier
normal_dim_tied_iteration256 10_Targets normal_dim_tied_iteration128 10_Targets normal_dim_tied_iteration64 10_Targets normal_dim_tied_iteration32 10_Targets normal_dim_tied_iteration256 Mnist normal_dim_tied_iteration128 Mnist normal_dim_tied_iteration64 Mnist normal_dim_tied_iteration32 Mnist
0 0.4168 0.419 0.4463 0.4005 0.4121 0.3612 0.2934 0.3231
1 0.4157 0.3629 0.3531 0.3123 0.2955 0.3235 0.1775 0.1873
2 0.4084 0.3431 0.313 0.2923 0.2277 0.3034 0.1581 0.1584
3 0.4053 0.3388 0.2993 0.2867 0.1998 0.2915 0.1524 0.1501
4 0.4023 0.3364 0.2961 0.2878 0.1829 0.2811 0.1483 0.1444
5 0.4004 0.3341 0.295 0.2875 0.1731 0.27 0.144 0.1388
6 0.3976 0.3344 0.2946 0.2873 0.1597 0.2557 0.1435 0.1351
7 0.3952 0.3343 0.2938 0.2873 0.1525 0.2473 0.1318 0.1277
Loss over iterations autoencoder
normal_dim_tied_iteration256 10_Targets normal_dim_tied_iteration128 10_Targets normal_dim_tied_iteration64 10_Targets normal_dim_tied_iteration32 10_Targets normal_dim_tied_iteration256 Mnist normal_dim_tied_iteration128 Mnist normal_dim_tied_iteration64 Mnist normal_dim_tied_iteration32 Mnist
0 0.36119 0.348424 2748.09 128.418 4936.27 242266 64221.1 88.9256
1 2.88879e+09 0.378737 6.15886e+16 5.82331e+16 4.39462e+15 1.4504e+18 1.00127e+18 2.2792e+10
2 1.76784e+22 0.397657 1.38208e+30 2.65066e+31 3.92542e+27 8.70153e+30 1.57148e+31 9.38586e+18
3 inf 0.410796 inf inf inf inf inf 3.86691e+27
4 inf 0.424774 inf inf inf inf inf inf
5 inf 0.433369 inf nan inf inf inf inf
6 inf 0.436835 nan nan nan nan nan inf
7 nan 0.437946 nan nan nan nan nan inf
MAE over iterations autoencoder
normal_dim_tied_iteration256 10_Targets normal_dim_tied_iteration128 10_Targets normal_dim_tied_iteration64 10_Targets normal_dim_tied_iteration32 10_Targets normal_dim_tied_iteration256 Mnist normal_dim_tied_iteration128 Mnist normal_dim_tied_iteration64 Mnist normal_dim_tied_iteration32 Mnist
0 0.270721 0.280256 1.60147 0.384265 3.50793 61.061 29.5184 2.7358
1 997.664 0.278353 6.33809e+06 2.00159e+06 3.08597e+06 1.55189e+08 1.2669e+08 62037.6
2 2.46756e+09 0.277853 3.0025e+13 4.27039e+13 2.91659e+12 3.80122e+14 5.01907e+14 1.26177e+09
3 6.10425e+15 0.277661 1.42233e+20 9.11087e+20 2.7565e+18 9.31058e+20 1.98839e+21 2.56116e+13
4 1.51007e+22 0.277708 6.73775e+26 1.9438e+28 2.6052e+24 2.2805e+27 7.87736e+27 5.19856e+17
5 3.7356e+28 0.277908 inf nan 2.4622e+30 inf inf 1.05519e+22
6 inf 0.278071 nan nan nan nan nan 2.14179e+26
7 nan 0.278143 nan nan nan nan nan 4.34733e+30
predictions_df_90
Accuracy over iterations evaluations_feature_classifier
normal_dim_tied_iteration256 10_Targets normal_dim_tied_iteration128 10_Targets normal_dim_tied_iteration64 10_Targets normal_dim_tied_iteration32 10_Targets normal_dim_tied_iteration256 Mnist normal_dim_tied_iteration128 Mnist normal_dim_tied_iteration64 Mnist normal_dim_tied_iteration32 Mnist
0 0.3568 0.3585 0.3858 0.3497 0.3576 0.3033 0.2643 0.2807
1 0.3566 0.3165 0.3156 0.2773 0.245 0.2706 0.1556 0.1532
2 0.3521 0.303 0.2789 0.2533 0.1883 0.26 0.1428 0.1335
3 0.3471 0.2976 0.2637 0.2487 0.1681 0.2479 0.1378 0.1283
4 0.3451 0.2937 0.2607 0.249 0.1525 0.2381 0.1347 0.1239
5 0.3428 0.2943 0.2602 0.2487 0.1444 0.2316 0.1317 0.121
6 0.3423 0.2946 0.2603 0.2487 0.1361 0.2135 0.1256 0.1174
7 0.3404 0.2945 0.2606 0.2488 0.1378 0.2076 0.1223 0.1161
Loss over iterations autoencoder
normal_dim_tied_iteration256 10_Targets normal_dim_tied_iteration128 10_Targets normal_dim_tied_iteration64 10_Targets normal_dim_tied_iteration32 10_Targets normal_dim_tied_iteration256 Mnist normal_dim_tied_iteration128 Mnist normal_dim_tied_iteration64 Mnist normal_dim_tied_iteration32 Mnist
0 0.723914 0.353494 8862.38 6064.18 13319.2 410721 114378 127.622
1 2.10742e+12 2.76552e+10 1.98685e+17 2.7594e+18 1.18623e+16 2.4594e+18 1.78621e+18 3.31004e+10
2 1.28967e+25 2.49982e+25 4.45858e+30 inf 1.05958e+28 1.47549e+31 2.80344e+31 1.3631e+19
3 inf inf inf inf inf inf inf 5.61588e+27
4 inf inf inf inf inf inf inf inf
5 inf inf inf nan inf inf inf inf
6 nan nan nan nan nan nan nan inf
7 nan nan nan nan nan nan nan inf
MAE over iterations autoencoder
normal_dim_tied_iteration256 10_Targets normal_dim_tied_iteration128 10_Targets normal_dim_tied_iteration64 10_Targets normal_dim_tied_iteration32 10_Targets normal_dim_tied_iteration256 Mnist normal_dim_tied_iteration128 Mnist normal_dim_tied_iteration64 Mnist normal_dim_tied_iteration32 Mnist
0 0.280915 0.283274 3.53546 1.15982 8.90369 97.2548 38.9854 3.50805
1 12401.7 1322.91 1.55567e+07 1.8453e+07 8.28434e+06 2.4694e+08 1.66078e+08 82183
2 3.06786e+10 3.97701e+10 7.36958e+13 3.93695e+14 7.82964e+12 6.04858e+14 6.57949e+14 1.6714e+09
3 7.58928e+16 1.1957e+18 3.49107e+20 8.39946e+21 7.39987e+18 1.48152e+21 2.60658e+21 3.39264e+13
4 1.87743e+23 3.59491e+25 1.65377e+27 1.79202e+29 6.99369e+24 3.62878e+27 1.03264e+28 6.88627e+17
5 4.64439e+29 inf inf nan 6.60981e+30 inf inf 1.39775e+22
6 nan nan nan nan nan nan nan 2.83711e+26
7 nan nan nan nan nan nan nan 5.75869e+30
predictions_df_100
Accuracy over iterations evaluations_feature_classifier
normal_dim_tied_iteration256 10_Targets normal_dim_tied_iteration128 10_Targets normal_dim_tied_iteration64 10_Targets normal_dim_tied_iteration32 10_Targets normal_dim_tied_iteration256 Mnist normal_dim_tied_iteration128 Mnist normal_dim_tied_iteration64 Mnist normal_dim_tied_iteration32 Mnist
0 0.2963 0.291 0.3201 0.2991 0.3121 0.2589 0.2221 0.2454
1 0.3045 0.261 0.2636 0.2468 0.2088 0.2251 0.1343 0.1277
2 0.2998 0.2534 0.2322 0.23 0.1652 0.2137 0.1248 0.1201
3 0.2991 0.2496 0.2191 0.2282 0.147 0.2081 0.1198 0.1176
4 0.2967 0.2476 0.2174 0.2289 0.1353 0.199 0.1172 0.1167
5 0.2953 0.2464 0.2167 0.2282 0.1266 0.1943 0.1156 0.1144
6 0.2936 0.2462 0.2166 0.2276 0.1206 0.1828 0.1108 0.1152
7 0.292 0.2464 0.2162 0.2276 0.1216 0.1775 0.106 0.1137
Loss over iterations autoencoder
normal_dim_tied_iteration256 10_Targets normal_dim_tied_iteration128 10_Targets normal_dim_tied_iteration64 10_Targets normal_dim_tied_iteration32 10_Targets normal_dim_tied_iteration256 Mnist normal_dim_tied_iteration128 Mnist normal_dim_tied_iteration64 Mnist normal_dim_tied_iteration32 Mnist
0 3.09084 0.367812 95160.2 0.34763 62374.5 947791 172409 176.613
1 1.64076e+13 3.2722e+12 2.13445e+18 3.10789e+06 5.56086e+16 5.67781e+18 2.69351e+18 4.64529e+10
2 1.00409e+26 2.95782e+27 4.7898e+31 1.41464e+21 4.96714e+28 3.40634e+31 4.22742e+31 1.91298e+19
3 inf inf inf inf inf inf inf 7.88136e+27
4 inf inf inf inf inf inf inf inf
5 inf inf inf inf inf inf inf inf
6 nan nan nan nan nan nan nan inf
7 nan nan nan nan nan nan nan inf
MAE over iterations autoencoder
normal_dim_tied_iteration256 10_Targets normal_dim_tied_iteration128 10_Targets normal_dim_tied_iteration64 10_Targets normal_dim_tied_iteration32 10_Targets normal_dim_tied_iteration256 Mnist normal_dim_tied_iteration128 Mnist normal_dim_tied_iteration64 Mnist normal_dim_tied_iteration32 Mnist
0 0.295359 0.2887 17.1987 0.300294 28.169 187.022 54.1196 4.27913
1 35141.5 18686.4 8.06622e+07 17.4209 2.67849e+07 4.72439e+08 2.28884e+08 102476
2 8.69332e+10 5.61821e+11 3.82115e+14 3.65014e+08 2.53147e+13 1.15719e+15 9.06765e+14 2.08401e+09
3 2.15055e+17 1.68913e+19 1.81013e+21 7.78756e+15 2.39252e+19 2.83439e+21 3.5923e+21 4.23015e+13
4 5.32003e+23 5.07844e+26 8.57484e+27 1.66147e+23 2.26119e+25 6.94245e+27 1.42315e+28 8.58623e+17
5 1.31607e+30 inf inf 3.54475e+30 2.13708e+31 inf inf 1.74281e+22
6 nan nan nan nan nan nan nan 3.53749e+26
7 nan nan nan nan nan nan nan 7.18029e+30
/home/hicky/anaconda3/lib/python3.7/site-packages/matplotlib/image.py:397: UserWarning: Warning: converting a masked element to nan.
  dv = (np.float64(self.norm.vmax) -
/home/hicky/anaconda3/lib/python3.7/site-packages/matplotlib/image.py:398: UserWarning: Warning: converting a masked element to nan.
  np.float64(self.norm.vmin))
/home/hicky/anaconda3/lib/python3.7/site-packages/matplotlib/image.py:405: UserWarning: Warning: converting a masked element to nan.
  a_min = np.float64(newmin)
/home/hicky/anaconda3/lib/python3.7/site-packages/matplotlib/image.py:410: UserWarning: Warning: converting a masked element to nan.
  a_max = np.float64(newmax)
<string>:6: UserWarning: Warning: converting a masked element to nan.
/home/hicky/anaconda3/lib/python3.7/site-packages/numpy/ma/core.py:711: UserWarning: Warning: converting a masked element to nan.
  data = np.array(a, copy=False, subok=subok)
/home/hicky/anaconda3/lib/python3.7/site-packages/matplotlib/image.py:397: UserWarning: Warning: converting a masked element to nan.
  dv = (np.float64(self.norm.vmax) -
/home/hicky/anaconda3/lib/python3.7/site-packages/matplotlib/image.py:398: UserWarning: Warning: converting a masked element to nan.
  np.float64(self.norm.vmin))
/home/hicky/anaconda3/lib/python3.7/site-packages/matplotlib/image.py:405: UserWarning: Warning: converting a masked element to nan.
  a_min = np.float64(newmin)
/home/hicky/anaconda3/lib/python3.7/site-packages/matplotlib/image.py:410: UserWarning: Warning: converting a masked element to nan.
  a_max = np.float64(newmax)
<string>:6: UserWarning: Warning: converting a masked element to nan.
/home/hicky/anaconda3/lib/python3.7/site-packages/numpy/ma/core.py:711: UserWarning: Warning: converting a masked element to nan.
  data = np.array(a, copy=False, subok=subok)
/home/hicky/anaconda3/lib/python3.7/site-packages/matplotlib/image.py:397: UserWarning: Warning: converting a masked element to nan.
  dv = (np.float64(self.norm.vmax) -
/home/hicky/anaconda3/lib/python3.7/site-packages/matplotlib/image.py:398: UserWarning: Warning: converting a masked element to nan.
  np.float64(self.norm.vmin))
/home/hicky/anaconda3/lib/python3.7/site-packages/matplotlib/image.py:405: UserWarning: Warning: converting a masked element to nan.
  a_min = np.float64(newmin)
/home/hicky/anaconda3/lib/python3.7/site-packages/matplotlib/image.py:410: UserWarning: Warning: converting a masked element to nan.
  a_max = np.float64(newmax)
<string>:6: UserWarning: Warning: converting a masked element to nan.
/home/hicky/anaconda3/lib/python3.7/site-packages/numpy/ma/core.py:711: UserWarning: Warning: converting a masked element to nan.
  data = np.array(a, copy=False, subok=subok)
/home/hicky/anaconda3/lib/python3.7/site-packages/matplotlib/image.py:397: UserWarning: Warning: converting a masked element to nan.
  dv = (np.float64(self.norm.vmax) -
/home/hicky/anaconda3/lib/python3.7/site-packages/matplotlib/image.py:398: UserWarning: Warning: converting a masked element to nan.
  np.float64(self.norm.vmin))
/home/hicky/anaconda3/lib/python3.7/site-packages/matplotlib/image.py:405: UserWarning: Warning: converting a masked element to nan.
  a_min = np.float64(newmin)
/home/hicky/anaconda3/lib/python3.7/site-packages/matplotlib/image.py:410: UserWarning: Warning: converting a masked element to nan.
  a_max = np.float64(newmax)
<string>:6: UserWarning: Warning: converting a masked element to nan.
/home/hicky/anaconda3/lib/python3.7/site-packages/numpy/ma/core.py:711: UserWarning: Warning: converting a masked element to nan.
  data = np.array(a, copy=False, subok=subok)
/home/hicky/anaconda3/lib/python3.7/site-packages/matplotlib/image.py:397: UserWarning: Warning: converting a masked element to nan.
  dv = (np.float64(self.norm.vmax) -
/home/hicky/anaconda3/lib/python3.7/site-packages/matplotlib/image.py:398: UserWarning: Warning: converting a masked element to nan.
  np.float64(self.norm.vmin))
/home/hicky/anaconda3/lib/python3.7/site-packages/matplotlib/image.py:405: UserWarning: Warning: converting a masked element to nan.
  a_min = np.float64(newmin)
/home/hicky/anaconda3/lib/python3.7/site-packages/matplotlib/image.py:410: UserWarning: Warning: converting a masked element to nan.
  a_max = np.float64(newmax)
<string>:6: UserWarning: Warning: converting a masked element to nan.
/home/hicky/anaconda3/lib/python3.7/site-packages/numpy/ma/core.py:711: UserWarning: Warning: converting a masked element to nan.
  data = np.array(a, copy=False, subok=subok)
/home/hicky/anaconda3/lib/python3.7/site-packages/matplotlib/image.py:397: UserWarning: Warning: converting a masked element to nan.
  dv = (np.float64(self.norm.vmax) -
/home/hicky/anaconda3/lib/python3.7/site-packages/matplotlib/image.py:398: UserWarning: Warning: converting a masked element to nan.
  np.float64(self.norm.vmin))
/home/hicky/anaconda3/lib/python3.7/site-packages/matplotlib/image.py:405: UserWarning: Warning: converting a masked element to nan.
  a_min = np.float64(newmin)
/home/hicky/anaconda3/lib/python3.7/site-packages/matplotlib/image.py:410: UserWarning: Warning: converting a masked element to nan.
  a_max = np.float64(newmax)
<string>:6: UserWarning: Warning: converting a masked element to nan.
/home/hicky/anaconda3/lib/python3.7/site-packages/numpy/ma/core.py:711: UserWarning: Warning: converting a masked element to nan.
  data = np.array(a, copy=False, subok=subok)
/home/hicky/anaconda3/lib/python3.7/site-packages/matplotlib/image.py:397: UserWarning: Warning: converting a masked element to nan.
  dv = (np.float64(self.norm.vmax) -
/home/hicky/anaconda3/lib/python3.7/site-packages/matplotlib/image.py:398: UserWarning: Warning: converting a masked element to nan.
  np.float64(self.norm.vmin))
/home/hicky/anaconda3/lib/python3.7/site-packages/matplotlib/image.py:405: UserWarning: Warning: converting a masked element to nan.
  a_min = np.float64(newmin)
/home/hicky/anaconda3/lib/python3.7/site-packages/matplotlib/image.py:410: UserWarning: Warning: converting a masked element to nan.
  a_max = np.float64(newmax)
<string>:6: UserWarning: Warning: converting a masked element to nan.
/home/hicky/anaconda3/lib/python3.7/site-packages/numpy/ma/core.py:711: UserWarning: Warning: converting a masked element to nan.
  data = np.array(a, copy=False, subok=subok)
/home/hicky/anaconda3/lib/python3.7/site-packages/matplotlib/image.py:397: UserWarning: Warning: converting a masked element to nan.
  dv = (np.float64(self.norm.vmax) -
/home/hicky/anaconda3/lib/python3.7/site-packages/matplotlib/image.py:398: UserWarning: Warning: converting a masked element to nan.
  np.float64(self.norm.vmin))
/home/hicky/anaconda3/lib/python3.7/site-packages/matplotlib/image.py:405: UserWarning: Warning: converting a masked element to nan.
  a_min = np.float64(newmin)
/home/hicky/anaconda3/lib/python3.7/site-packages/matplotlib/image.py:410: UserWarning: Warning: converting a masked element to nan.
  a_max = np.float64(newmax)
<string>:6: UserWarning: Warning: converting a masked element to nan.
/home/hicky/anaconda3/lib/python3.7/site-packages/numpy/ma/core.py:711: UserWarning: Warning: converting a masked element to nan.
  data = np.array(a, copy=False, subok=subok)